Deep Learning Based Biomedical NER Framework

Robert Phan, Thoai Man Luu, Rachel Davey, Girija Chetty

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

4 Citations (Scopus)


This paper describes a novel deep learning-based framework for biomedical name entity recognition. Bio-Entity name entity recognition task based on three different deep learning techniques: Feedforward Networks (FFNs), Recurrent Neural Networks (RNNs), and Hybrid Convolutional Neural Networks (CNNs), has allowed better latent feature learning and discovery for the complex NLP task. The performance evaluation of the proposed framework with the BioNLP dataset corresponding to biomedical entity recognition task, has led to promising performance, when assessed in terms of F-measure, Recall and Precision. The best performing deep learner based on Hybrid CNN approach has resulted in an F-score of 70.32%, and surpassed the performance reported by other participants in the Challenge task.
Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
EditorsSuresh Sundaram
Place of PublicationBangalore, India
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781538692769
ISBN (Print)9781538692769
Publication statusPublished - 18 Nov 2018
Event2018 IEEE Symposium Series on Computational Intelligence - Sheraton Grand Bangalore Hotel, Bengalore, India
Duration: 18 Nov 201821 Nov 2018

Publication series

NameProceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018


Conference2018 IEEE Symposium Series on Computational Intelligence
Abbreviated titleIEEE-SSCI 2018
Internet address


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